低盐度水泛滥:使用粒子群优化和机器学习技术的合来估计相对透率和毛细血管压力
Razieh Khosravi1, Mohammad Simjoo2, Mohammad Chahardowli1
1Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran.
Scientific reports
|June 8, 2024
概括
这项研究开发了一种混合方法,用于估计低盐水洪水的相对透性和毛细血管压力. 该方法准确地预测了这些关键的储特性,有助于改进石油回收模拟.
科学领域:
- 石油工程是石油工程中的一个.
- 储水池模拟器 储水池模拟器
- 增强的石油回收 (EOR)
背景情况:
- 储模拟需要准确的相对透率和毛细血管压力数据,这些数据很难通过实验获得.
- 现有的实验方法昂贵且耗时,需要采用替代方法.
研究的目的:
- 为了确定在低盐度水洪流下砂岩核心的相对透性和毛细血管压力函数.
- 研究粘土存在和盐度变化对这些功能的影响.
主要方法:
- 将一个水库模拟器与粒子群优化相结合,实现自动历史匹配.
- 开发用于低盐度条件的多重线性回归的相关性.
- 验证与实验数据对无粘土和含粘土形成的相关性.
主要成果:
- 相关性实现了相对透性和毛细血管压力参数的高验证准确性 (95%和97%).
- 降低盐度显著影响了水的相对透性,而不是石油的相对透性.
- 粘土含量导致相对透度曲线明显向较高和度转移.
结论:
- 拟议的混合方法有效地估计了相对透性和毛细血管压力功能.
- 这种方法对于模拟低盐度水的洪水和优化基于水的EOR策略是有价值的.
- 了解盐度和粘土的影响对于准确预测水库性能至关重要.
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